Computed tomography (CT) systems and methods are widely used, particularly for medical imaging and diagnosis. CT systems generally create projection images through a subject's body at a series of projection angles. A radiation source, such as an X-ray tube, irradiates the body from one side. Images of the subject's body can be reconstructed from the projection data (i.e., the projection images acquired a various projection angles), using various reconstruction techniques such as filtered back-projection, iterative reconstruction, etc.
Conventionally energy-integrating detectors have been used to measure CT projection data. Now, recent technology developments are making, photon-counting detectors a feasible alternative to conventional energy-integrating detectors. Photon-counting detectors have many advantages including their capacity for performing spectral CT, wherein the photon-counting detectors resolve the counts of incident X-rays into spectral components referred to as energy bins. Each energy bin has a different spectral window within the energy spectrum of the X-ray beam, such that collectively the energy bins span the energy spectrum of the X-ray beam. Since spectral CT involves the detection of transmitted X-rays at two or more energy levels, spectral CT generally includes dual-energy CT by definition.
Photon-counting detectors can use semiconductors with fast response times. This fast response time enables photon-counting detectors to resolve in time individual X-ray detection events. However, at high X-ray flux rates indicative of clinical X-ray imaging, multiple X-ray detection events on a single detector can occur within the detector's time response—a phenomenon called pileup. Left uncorrected, pileup, detector nonlinearities, and other artifacts of the projective imaging process can degrade reconstructed images from photon-counting detectors.
On the other hand, when these effects are corrected, spectral CT has many advantages over conventional CT. Many clinical applications can benefit from spectral CT technology, including improved material differentiation and beam hardening corrections. Moreover, compared with non-spectral CT, spectral CT extracts complete tissue characterization information from an imaged object.
One challenge for more effectively using semiconductor-based photon-counting detectors for spectral CT is performing the material decomposition of the projection data in a robust and efficient manner. For example, correction of pileup and nonlinearities in the X-ray detection process can be imperfect, and these imperfections degrade the material components resulting from the material decomposition.
To avoid the biases and errors associated with pileup, post-selection and/or pileup rejection can be used to isolate those detection events in which a single X-ray photon was detected within a detection time window and exclude the multi-photon detection events. The projection data corresponding to only single-photon detection events is called singles-counts projection data, in contrast to the total-counts projection data.
One drawback of the singles-counts projection data is that the input X-ray flux resulting in a given number of singles counts is multi-valued, resulting in a multi-valued material decomposition. Thus, material decomposition using singles counts (as opposed to total counts data) can generate ambiguous results. On the other hand, material decomposition using total-counts projection data generates a unique solution, but this unique solution suffers from biases, increased noise, and other errors introduced by pileup. Conventional methods do not simultaneously achieve the enhanced precision of material decomposition using singles counts together with the uniqueness of material decomposition using the total counts.